1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

1.2 MERSCOPE Data Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

2.1 Cell Segmentation

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 1800], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

2.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

3.1 Cell Annotation (Low resolution)

3.2 Cell Annotation (High resolution)

3.3 Finalized cell type Umap

3.4 Spatial cell type (All)

3.5 Spatial cell type (Glut)

3.6 Spatial cell type (Non-Neuron)

3.7 Cell Type Qualification Analysis

3.8 Cell Type Proportion Analysis

Based on the quantification of individual cell types, we are capable to compare the differences in cell numbers between various samples and genotypes. The proportion of each cell type is calculated by dividing the count of that specific cell type by the total cell count. The results are then visualized in a bar plot.

Notably, B cells, mast cells, and T cells show an obvious increase in tumor samples.

4.1 Astrocyte

4.2 CA1 Glut

4.3 CA3 Glut

4.4 CB Neuron

4.5 CHOR

4.6 DG Glut

4.7 Endothelial

4.8 L2_3 IT Glut

4.9 L4_5 IT Glut

4.10 L5 ET Glut

4.11 L6 CT Glut

4.12 L6 IT Glut

4.13 L6b Glut

4.14 Microglia

4.15 MY_MB_P_HY Neuron

4.16 Oligo

4.17 OPC

4.18 Pericyte

4.19 Pvalb GABA

4.20 Sst GABA

4.21 STR Neuron

4.22 SUB Glut

4.23 TH Neuron

4.24 Vip_Lamp5_Sncg GABA

4.25 VLMC

5.1 Hippocampus Region

5.2 Cell Type Qualification

5.3 Cell Type Proportion

6.1 Astrocyte

6.2 CA1 Glut

6.3 CA3 Glut

6.4 DG Glut